Update README.md

README.md

@@ -1,10 +1,85 @@
----
-license: apache-2.0
-datasets:
-- bengsoon/volve_alpaca
-language:
-- en
-base_model:
-- NousResearch/Meta-Llama-3-8B
-pipeline_tag: summarization
----
+---
+license: apache-2.0
+datasets:
+- bengsoon/volve_alpaca
+language:
+- en
+base_model:
+- Meta/Meta-Llama-3-8B
+pipeline_tag: summarization
+tags:
+- oil-and-gas
+- energy
+- drilling
+---
+# DriLLM Summarizer
+
+## Background
+This is a fine-tuned model from [Meta/Meta-Llama-3-8B](https://huggingface.co/Meta/Meta-Llama-3-8B). The model was finetuned using [Volve DDR dataset](https://huggingface.co/datasets/bengsoon/volve_alpaca) using the Alpaca template, using [Axolotl](https://github.com/axolotl-ai-cloud/axolotl).
+
+The motivation behind this model was to fine-tune an LLM that is capable of understanding the nuances of the Drilling Operations and provide summarizations based on the inputs from Daily Drilling Reports hourly activites.
+
+## How to use
+### Recommended template for DriLLM summarization:
+``` python
+TEMPLATE = """<|begin_of_text|>Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
+
+### Instruction:
+{instruction}
+
+
+### Input:
+{input}
+
+
+### Response:
+
+"""
+```
+
+### Inferencing using Transformers Pipeline
+``` python
+import transformers
+import torch
+
+model_id = "bengsoon/DriLLM-Summarizer"
+
+pipeline = transformers.pipeline(
+    "text-generation", model=model_id, model_kwargs={"torch_dtype": torch.bfloat16}, device_map="auto"
+)
+
+TEMPLATE = """<|begin_of_text|>Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
+
+### Instruction:
+{instruction}
+
+
+### Input:
+{input}
+
+
+### Response:
+
+"""
+
+INSTRUCTION = """You are a Rig Supervisor working at an oil and gas offshore drilling operation. \
+Your company is currently on a drilling campaign and you are the on-site Drilling Engineer (DE). \
+As a DE, one of your jobs is to oversee the operations at the drilling rigs. As such, you know the ins and outs of the operation, down to the hourly activities. \
+Every day, activities are recorded either by the Driller, Mud Logger, MWD / LWD engineer or the Drilling Operations Coordinator throughout the day. \
+As a DE representative for your company, you are required to prepare the 24-hour summary for the Daily Drilling Report (DDR) based on the hourly activities reported. \
+You must always maintain the language of report along with the terminologies and mnemonics of the Drilling Engineer. \
+Given the following activities for well XX, please prepare the 24-hour summary for the Daily Drilling Report (DDR). \
+Only return the 24-hour summary, and nothing else.
+"""
+
+hourly_events = """00:00 - 11:00: Packed equipment and prepared for backload. Cleaned drillfloor and cantilever.
+11:00 - 17:00: Performed are inspection with barge engineer. Cleaned and tidied offices and workspace. Demobilized all personell. End of operation
+"""
+
+input = TEMPLATE.format(instruction=INSTRUCTION, input=hourly_events)
+
+output = pipeline(input)
+
+print("Response: ", output[0]["generated_text"].split("### Response:")[1].strip())
+# > Response:  Packed equipment and prepared for backload. Cleaned drillfloor and cantilever. Performed are inspection with barge engineer. Cleaned and tidyied offices and workspaces.
+```